改变一列模型：“错误：`mutate()` 输入 `model` 出现问题。x 输入 `model` 必须是向量，而不是 `lm` 对象。”

Question

我有一个数据框，其中包含模型公式定义作为列。我想改变一个新列，其中每一行都是基于相应行模型定义的模型。

一些数据：

# Set up
library(tidyverse)
library(lubridate)


# Create data
mydf <- data.frame(
  cohort = seq(ymd('2019-01-01'), ymd('2019-12-31'), by = '1 days'),
  n = rnorm(365, 1000, 50) %>% round,
  cohort_cost = rnorm(365, 800, 50)
) %>% 
  crossing(tenure_days = 0:365) %>% 
  mutate(activity_date = cohort + days(tenure_days)) %>% 
  mutate(daily_revenue = rnorm(nrow(.), 20, 1)) %>% 
  group_by(cohort) %>% 
  arrange(activity_date) %>% 
  mutate(cumulative_revenue = cumsum(daily_revenue)) %>% 
  arrange(cohort, activity_date) %>% 
  mutate(payback_velocity = round(cumulative_revenue / cohort_cost, 2)) %>% 
  select(cohort, n, cohort_cost, activity_date, tenure_days, everything())

## wider data
mydf_wide <- mydf %>% 
  select(cohort, n, cohort_cost, tenure_days, payback_velocity) %>% 
  group_by(cohort, n, cohort_cost) %>% 
  pivot_wider(names_from = tenure_days, values_from = payback_velocity, names_prefix = 'velocity_day_')

现在，最后一个问题代码块。它在最后一行失败：

models <- data.frame(
  from = mydf$tenure_days %>% unique,
  to = mydf$tenure_days %>% unique
) %>% 
  expand.grid %>% 
  filter(to > from) %>% 
  filter(from > 0) %>% 
  arrange(from) %>% 
  mutate(mod_formula = paste0('velocity_day_', to, ' ~ velocity_day_', from)) %>% 
  mutate(model = lm(as.formula(mod_formula), data = mydf_wide))

错误：mutate() 输入 model 有问题。 x 输入 model 必须是向量，而不是 lm 对象。 ℹ 输入model 是lm(as.formula(mod_formula), data = mydf_wide)。

如果我运行最后一个代码块减去最后一行并查看生成的数据框“模型”，它看起来像这样：

models %>% head
  from to                     mod_formula
1    1  2 velocity_day_2 ~ velocity_day_1
2    1  3 velocity_day_3 ~ velocity_day_1
3    1  4 velocity_day_4 ~ velocity_day_1
4    1  5 velocity_day_5 ~ velocity_day_1
5    1  6 velocity_day_6 ~ velocity_day_1
6    1  7 velocity_day_7 ~ velocity_day_1

我尝试将其设为列表列，但据我所知，我需要分组。但在这种情况下，我需要按所有内容分组。我修改了最后一个代码块：

models <- data.frame(
  from = mydf$tenure_days %>% unique,
  to = mydf$tenure_days %>% unique
) %>% 
  expand.grid %>% 
  filter(to > from) %>% 
  filter(from > 0) %>% 
  arrange(from) %>% 
  mutate(mod_formula = paste0('velocity_day_', to, ' ~ velocity_day_', from)) %>% 
  group_by_all() %>% 
  nest() %>% 
  mutate(model = lm(as.formula(mod_formula), data = mydf_wide))

但是这会导致同样的错误。

如何根据字段“mod_formula”中的公式将新列添加到“模型”中，该列包含每行的线性模型？

Answer 1

lm 未矢量化。添加rowwise 为每一行创建一个模型。

library(dplyr)

models <- data.frame(
  from = mydf$tenure_days %>% unique,
  to = mydf$tenure_days %>% unique
) %>% 
  expand.grid %>% 
  filter(to > from) %>% 
  filter(from > 0) %>% 
  arrange(from) %>% 
  mutate(mod_formula = paste0('velocity_day_', to, ' ~ velocity_day_', from)) %>%
  rowwise() %>%
  mutate(model = list(lm(as.formula(mod_formula), data = mydf_wide)))

models

#  from    to mod_formula                     model 
#  <int> <int> <chr>                           <list>
#1     1     2 velocity_day_2 ~ velocity_day_1 <lm>  
#2     1     3 velocity_day_3 ~ velocity_day_1 <lm>  
#3     1     4 velocity_day_4 ~ velocity_day_1 <lm>  
#4     1     5 velocity_day_5 ~ velocity_day_1 <lm>  
#5     1     6 velocity_day_6 ~ velocity_day_1 <lm>  
#6     1     7 velocity_day_7 ~ velocity_day_1 <lm>  
#...
#...

---

您也可以使用map 代替rowwise。

mutate(model = purrr::map(mod_formula, ~lm(.x, data = mydf_wide)))